Técnicas de aprendizaje automático para el análisis de datos de calidad del aire

La contaminación del aire supone, cada vez más, una amenaza a la salud humana y una preocupación medioambiental para el futuro. En concreto el dióxido de nitrógeno (NO2), generado principalmente por el tráfico en zonas urbanas, alcanza en la actualidad niveles peligrosos para la salud. Los gobiernos de diversos países y ciudades han propuesto medidas para reducir esta contaminación. Es destacable el caso de la ciudad de Madrid, obligada a cumplir la normativa europea que impone unos niveles máximos de NO2 en las ciudades. Para hacer una mejor gestión de las medidas, que implican limitaciones en términos de velocidad máxima en algunas vías o incluso restricciones de acceso y/o aparcamientos en el centro de la ciudad, es especialmente interesante la capacidad de obtener predicciones precisas de la concentración de NO2 en la ciudad. En esta labor juega un papel fundamental el aprendizaje automático, una rama de la inteligencia artificial encargada del diseño de modelos de predicción, que son esenciales para anticiparse a los niveles de contaminación. Portanto,estetrabajotieneelobjetivodeconstruirunmodelopredictivodelaconcentración de NO2. Para ello se utilizan datos históricos de la concentración de NO2 en la ciudad, datos reales del tráfico y predicciones de la meteorología en Madrid. Previamente a la construcción del modelo, se han analizado las características de los datos utilizados así como de sus fuentes. El Ayuntamiento de Madrid, en una iniciativa por promover la ciencia de datos, ofrece de forma gratuita la información del tráfico y la calidad del aire en la ciudad, obtenida a través de estaciones y sensores colocados en distintas zonas. Los datos de predicciones meteorológicas se obtienen de forma gratuita de la Administración Nacional Oceánica y Atmosférica (NOAA) de Estados Unidos. En este primer estudio se restringe el modelo a cuatro estaciones de calidad del aire ubicadas en Plaza de España, Escuelas Aguirra, Méndez Álvaro y Plaza del Carmen, utilizando la carga de tráfico en los puntos de medida más cercanos. De las predicciones meteorológicas se utilizan variables de temperatura, presión, velocidad del viento y humedad relativa. A partir de esta información se puede predecir la concentración de NO2 con relativo éxito. Utilizando una regresión lineal con la regularización de ElasticNet se obtienen predicciones con un error relativo medio (MRE o MAPE) del 20%, se ha comprobado que los modelos que incluyen las variables del tráfico mejoran a los que únicamente se basan en los datos históricos del NO2, mientras que la inclusión de datos de predicciones meteorológicas no es tan relevante en los resultados. Además se ha implementado un modelo de clasificación de alertas que, utilizando el algoritmo XGBoost, consigue predecir correctamente la mayoría de las alertas. Los resultados obtenidos en este TFG son prometedores y cabe esperar que la inclusión de nuevas variables y/o el uso de otros modelos de aprendizaje automático den lugar a sistemas de predicción más precisos.Pollution is an increasing thread for human health and a environmental concern for the future. In particular, the nitrogen dioxide, which is generated mainly by traffic in urban areas, is reaching levels that are dangerous for health. Governments of diverse countries and cities have already established laws in order to reduce these pollution levels. The city of Madrid, which has to to obey the European laws that establish maximum levels of NO2 in cities, is a remarkable case. Therefore, accurate predictions of NO2 concentration are interesting in order to improve management asociated with the anti-pollution measures, which include speed limits in some roads or even access and parking restrictions in the center of Madrid. To achieve this goal, machine learning plays a crucial role. Machine learning is a branch of artificial intelligence that explores the study and construction of predictive modelling, which are essential in order to know in advance the levels of NO2. The objective of this work is to create a predictive model for NO2 concentration. For this purpose, historic NO2 concentration data, real traffic data and metereology prediction data in the city of Madrid are used in this work. Before generating the model, the characteristics of the data contained in are carefully analyzed. The local government of Madrid offers for free the information about traffic and air quality, which are registered in numerous sensors and stations placed in the city. The metereological predictions are obtained freely from the National Ocenaic and Atmosferic Administration (NOAA) of the United States. InthisfirstworkthemodelisrestrictedtofourairqualitystationsplacedinPlazadeEspaña, Escuelas Aguirre, Méndez Álvaro and Plaza del Carmen, using the traffic load from their nearest traffic sensors. Metereological features of temperature, wind speed, pressure and relative humidity are used as well. Based on this information, it is possible to predict the NO2 concentration with relative accuracy. Using a linear regression algorithm with the ElasticNet regularization a mean relative error (MRE or MAPE) of 20% is obtained. Additionally it has been shown that models that include features related to traffic are better than those that only use historic NO2 concentration data. Furthermore, an alert classification model has been implemented. This model, uses the XGBoost algorithm, and predicts accurately the majority of alerts. The results obtained in this TFG are promising, and it is expected that the inclusion of new features or the use of different models will give as a result more accurate prediction systems

Support Vector Machines and Multi-Task Learning

En la mayoría de casos los problemas que se resuelven utilizando aprendizaje automático no están aislados, sino que hay numerosas tareas similares con las que están relacionados. El aprendizaje multitarea es una aproximación del aprendizaje automático que trata de resolver múltiples tareas al mismo tiempo, logrando así una perspectiva más amplia del problema global. Las máquinas de vectores soporte son unos de los métodos más populares en aprendizaje automático, y han demostrado ser modelos útiles que además están apoyados por la teoría de optimización convexa. En este trabajo estudiamos la adaptación de las máquinas de vectores soporte con el objetivo de encajar en un entorno multitarea. Nuestro primer paso para lograr esto es explorar la teoría de optimización convexa, específicamente las máquinas de vectores soporte. Hacemos un resumen de los problemas de optimización, incluyendo las condiciones KKT, así como el truco del kernel y el algoritmo SMO, que es el más popular para entrenar SVMs. Basamos nuestro estudio en dos adaptaciones previas de las SVMs al aprendizaje multitarea, desarrollando sus ideas y presentando las similitudes y diferencias entre ambas aproximaciones. También analizamos la relación entre los enfoques para obtener las ventajas que cada uno ofrece. Para probar la precisión de las SVMs multitarea proponemos dos experimentos utilizando datos reales. El primero utiliza datos de estudiantes de institutos de Inglaterra y el objetivo es predecir los resultados de los estudiantes en un test específico. En estos experimentos se utilizan datos de estudiantes de 139 institutos, y la predicción de las notas en cada instituto se observa como una tarea distinta. El segundo experimento consiste en predecir la producción solar, medida como un porcentaje, en dos islas de España: Mallorca y Tenerife. Aunque ambas islas pertenecen a España la distancia entre ellas es de más de 2.200 km. Esto hace que las predicciones en cada isla sean tareas separadas. En estos experimentos comparamos el enfoque multitarea con un enfoque global monotarea y, cuando es posible, entrenando un modelo distinto para cada tarea. A partir de los resultados obtenidos podemos observar que el enfoque multitarea obtiene mejores resultados que el enfoque global monotarea. En el caso de los datos de institutos obtenemos un MAE de 8,226 puntos usando el enfoque monotarea y uno de 8,039 puntos con el multitarea. Además, esta mejora tiene lugar no solo globalmente sino también en la comparación tarea a tarea el enfoque multitarea obtiene mejores resultados en 90 de las 139 tareas. En el experimento solar obtenemos una mejora de 0.45% en Tenerife usando el modelo multitarea, mientras que en Mallorca el error es 0.15% mayor; por tanto, se obtiene globalmente una mejora de 0.15 %. Estos resultados dan la motivación para una investigación futura con el objetivo de desarrollar todo el potencial de las SVMs multitarea.In most cases the problems we solve using machine learning are not isolated; instead there are several similar and related tasks. Multi-task learning is an approach of machine learning that tries to solve multiple related tasks at the same time, achieving a broader perspective of the global problem. Support vector machines are one of the most popular methods in machine learning, and they have proven to be really useful models which are also supported by the convex optimization theory. In this work we study the adaptation of support vector machines in order to match a multi-task learning framework. Our rst step to achieve this goal is to explore convex optimization theory and more speci cally, support vector machines theory. We will give an overview of the optimization problems, including the KKT conditions, as well as the kernel trick and the SMO algorithm, the most used algorithm to train SVMs. We base our study in two previous adaptations of SVMs to multi-task learning, developing their ideas and presenting the similarities and di erences between the two approaches. We also analyze the relation between the approaches and the possible advantages each one o ers. To test the accuracy of multi-task SVMs we propose two experiments using real data. The rst one uses data from English school students and the goal is to predict the results of the students in a speci c test. In this experiment, 139 di erent schools are used and predicting the marks of their students in each one of them is seen as a di erent task. Our second experiment consists on predicting the solar production measured as a percentage in two di erent islands of Spain: Majorca and Tenerife. Although both islands are part of Spain, the distance between both is roughly 2,200 km, making the predictions separate tasks. In our experiments we compare the multi-task approach with a single-task global approach and, when possible, with a model specialized for each task. From the results obtained we can observe that multi-task approach gets better results than a single-task global approach. In the case of the school data we get a MAE of 8.226 points using the single-task approach while the multi-task approach achieves 8.039. Moreover, we see that this improvement takes place not only globally, but in a task by task comparison the multi-task approach performs better in 90 tasks out of 139. In the solar experiment we obtain an improvement of 0.45% in the MAE of the prediction in Tenerife using the multi-task model, while the error in Majorca is 0.15% worse than using a single-task approach, resulting in a global improvement of 0.15%. This results give the motivation to do further research in this area with the goal of nding the full potential of multi-task SVMs

Structure Learning in Deep Multi-Task Models

Multi-Task Learning (MTL) aims at improving the learning process by solving different tasks simultaneously. Two general approaches for neural MTL are hard and soft information sharing during training. Here we propose two new approaches to neural MTL. The first one uses a common model to enforce a soft sharing learning of the tasks considered. The second one adds a graph Laplacian term to a hard sharing neural model with the goal of detecting existing but a priori unknown task relations. We will test both tasks on real and synthetic datasets and show that either one can improve on other MTL neural models.The authors acknowledge support from the European Regional Development Fund and the Spanish State Research Agency of the Ministry of Economy, Industry, and Competitiveness under the project PID2019-106827GB-I00. They also thank the UAM–ADIC Chair for Data Science and Machine Learning and gratefully acknowledge the use of the facilities of Centro de Computación Científica (CCC) at UAM

Participation in contests as teaching methodology for project-based learning in Bachelor’s Degree in Industrial Design and Product Development Engineering

Comunicación presentada en XXI Congreso Internacional de Dirección e Ingeniería de Proyectos, celebrado en Cádiz del 12 al 14 de julio de 2017 / Proceedings from the 21st International Congress on Project Management and Engineering (Cádiz, July 2017)El aprendizaje basado en proyectos es una metodología docente muy empleada hoy en día en la enseñanza en ingeniería por su capacidad para facilitar la adquisición de competencias, tales como la capacidad de investigación, toma de decisiones, trabajo en equipo, autodesarrollo, etc. Para ello, la elección de un proyecto que sea capaz de motivar suficientemente al alumnado se convierte en un punto clave, puesto que cuanto mayor sea la motivación del alumno para realizar bien un proyecto, mayor será su implicación y, por tanto, su aprendizaje. En el grado de Ingeniería en Diseño Industrial y Desarrollo de Producto de la Universitat Jaume I de Castellón se lleva empleando, en varias asignaturas y desde hace unos años, la participación en concursos como metodología docente para la enseñanza basada en proyectos. El presente artículo muestra un estudio basado en la experiencia en estas asignaturas sobre las ventajas que presenta este tipo de recurso docente.Project-based learning is a teaching methodology used today in enginnering education for its capacity to facilitate the acquisition of competences, such as the ability to research, decisionmaking, teamwork, self-development, etc. To do this, choosing a project that is able to sufficiently motivate students becomes a key point, since the higher the student's motivation to perform well a project, the greater their involvement and, therefore, their learning will be. The Bachelor’s Degree in Industrial Design and Product Development Engineering of the Universitat Jaume I of Castellón is using, in several subjects and from a few years ago, the participation in contests as teaching methodology for the project based learning. This paper shows a study based on the experience in these subjects on the advantages of this type of teaching resourc

Unsupervised Neural Networks for Identification of Aging Conditions in Li-Ion Batteries

This paper explores a new methodology based on data-driven approaches to identify and track degradation processes in Li-ion batteries. Our goal is to study if it is possible to differentiate the state of degradation of cells that present similar aging in terms of overall parameters (similar remaining capacity, state of health or internal resistance), but that have had different applications or conditions of use (different discharge currents, depth of discharges, temperatures, etc.). For this purpose, this study proposed to analyze voltage waveforms of cells obtained in cycling tests by using an unsupervised neural network, the Self-Organizing Map (SOM). In this work, a laboratory dataset of real Li-ion cells was used, and the SOM algorithm processed battery cell features, thus carrying out smart sensing of the battery. It was shown that our methodology differentiates the previous conditions of use (history) of a cell, complementing conventional metrics such as the state of health, which could be useful for the growing second-life market because it allows for determining more precisely the state of disease of a battery and assesses its suitability for a specific application

Lithium-Ion Battery Parameter Identification via Extremum Seeking Considering Aging and Degradation

Battery parameters such as State of Charge (SoC) and State of Health (SoH) are key to modern applications; thus, there is interest in developing robust algorithms for estimating them. Most of the techniques explored to this end rely on a battery model. As batteries age, their behavior starts differing from the models, so it is vital to update such models in order to be able to track battery behavior after some time in application. This paper presents a method for performing online battery parameter tracking by using the Extremum Seeking (ES) algorithm. This algorithm fits voltage waveforms by tuning the internal parameters of an estimation model and comparing the voltage output with the real battery. The goal is to estimate the electrical parameters of the battery model and to be able to obtain them even as batteries age, when the model behaves different than the cell. To this end, a simple battery model capable of capturing degradation and different tests have been proposed to replicate real application scenarios, and the performance of the ES algorithm in such scenarios has been measured. The results are positive, obtaining converging estimations both with new and aged batteries, with accurate outputs for the intended purpose

From Messengers to Receptors in Psoriasis: The Role of IL-17RA in Disease and Treatment.

The paradigm of psoriasis as a Th17-driven disease has evolved in the last years towards a much deeper knowledge of the complex pathways, mechanisms, cells, and messengers involved, highlighting the crucial role played by the IL-17 family of cytokines. All IL-17 isoforms signal through IL-17R. Five subunits of IL-17R have been described to date, which couple to form a homo- or heteroreceptor complex. Characteristically, IL-17RA is a common subunit in all hetero-receptors. IL-17RA has unique structural—containing a SEFIR/TILL domain—and functional—requiring ACT-1 for signaling—properties, enabling Th17 cells to act as a bridge between innate and adaptive immune cells. In psoriasis, IL-17RA plays a key role in pathogenesis based on: (a) IL-17A, IL-17F, and other IL-17 isoforms are involved in disease development; and (b) IL-17RA is essential for signaling of all IL-17 cytokines but IL-17D, whose receptor has not been identified to date. This article reviews current evidence on the biology and role of the IL-17 family of cytokines and receptors, with focus on IL-17RA, in psoriasis and some related comorbidities, and puts them in context with current and upcoming treatments.post-print1.096 K

Aplicación de mindfulness en la discapacidad intelectual: Un estudio preliminar

Mindfulness es un tipo de meditación que nos invita a ser conscientes del momento presente para reconocer nuestros propios estados mentales surgidos en cada situación, aceptándolos sin enjuiciamiento. El creciente interés despertado por la práctica de mindfulness ha incitado a diversos investigadores a indagar sobre este tema. El presente trabajo pretende realizar una aportación preliminar aplicando un programa de habilidades mindfulness en el contexto de la discapacidad intelectual. Para ello se ha procedido inicialmente a abordar la conceptualización del término mindfulness y de la discapacidad intelectual, una revisión de diferentes estudios aplicados en el contexto de la discapacidad intelectual y se ha diseñado e implementado un estudio preliminar de caso único incluyendo un participante control. A pesar de las limitaciones del estudio, los resultados obtenidos son alentadores respecto a la posibilidad de adaptar estas técnicas a este tipo de población y procurar posible mejoras

Correction: Vidal et al. From Messengers to Receptors in Psoriasis: The Role of IL-17RA in Disease and Treatment. Int. J. Mol. Sci. 2021, 22, 6740.

post-print599 K

Ocean-atmosphere exchange of organic carbon and CO2 surrounding the Antarctic Peninsula

Exchangeable organic carbon (OC) dynamics and CO2 fluxes in the Antarctic Peninsula during austral summer were highly variable, but the region appeared to be a net sink for OC and nearly in balance for CO2. Surface exchangeable dissolved organic carbon (EDOC) measurements had a 43±3 (standard error, hereafter SE) μmol CL-1 overall mean and represented around 66% of surface non-purgeable dissolved organic carbon (DOC) in Antarctic waters, while the mean concentration of the gaseous fraction of organic carbon (GOCH′-1) was 46±3 SE μmol C L -1. There was a tendency towards low fugacity of dissolved CO 2 (fCO2-w) in waters with high chlorophyll a (Chl a) content and high fCO2-w in areas with high krill densities. However, such relationships were not found for EDOC. The depth profiles of EDOC were also quite variable and occasionally followed Chl a profiles. The diel cycles of EDOC showed two distinct peaks, in the middle of the day and the middle of the short austral dark period, concurrent with solar radiation maxima and krill night migration patterns. However, no evident diel pattern for GOC H′-1 or CO2 was observed. The pool of exchangeable OC is an important and active compartment of the carbon budget surrounding the Antarctic Peninsula and adds to previous studies highlighting its importance in the redistribution of carbon in marine environments. © Author(s) 2014.This is a contribution of both Aportes Atmosféricos de Carbono Orgánico y Contaminanates al océano Polar (ATOS) and the Spanish component of the Synoptic Antarctic Shelf-Slope Interactions study (ESASSI), funded by the Spanish Ministry of Science under the scope of the International Polar Year (IPY). Maria Ll. Calleja was funded by the Spanish Research Council (CSIC, grant JAEDOC030) and cofounded by the Fondo Social Europeo (FSO)Peer Reviewe